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Enregistrement W4411674835 · doi:10.34067/kid.0000000757

Patient Perception of Dialysis Experience

2025· article· en· W4411674835 sur OpenAlexaboutno aff
Edwina A. Brown, Jessica Selwood

Notice bibliographique

RevueKidney360 · 2025
Typearticle
Langueen
DomaineHealth Professions
ThématiquePatient Satisfaction in Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDialysisPerceptionMedicinePsychologyInternal medicineNeuroscience

Résumé

récupéré en direct d'OpenAlex

Introduction Those in ethnic minority groups can be underserved by preexisting health care systems, and the increasing diversity of populations worldwide has necessitated research focusing on the interplay of ethnicity with health, including clinical outcomes and quality of life (QoL) measures. Better understanding of the role of culture and ethnicity on health is paramount in reducing health inequalities and ensuring equal access to services across ethnic groups. At the same time, rates of CKD and ESKD have increased, with an often disproportionate effect on those in ethnic minority groups.1 Although a plethora of data exists from countries such as the United States and the United Kingdom, this is not fully generalizable to other diverse populations across the world where different models of health care services exist. The recent study by Cohen-Hagai et al.2 examined a previously understudied group of maintenance hemodialysis patients in Israel to determine whether there was evidence of a relationship between ethnicity and patient satisfaction between Israeli Jewish and Arabic populations (n=74, n=53). The secondary outcome measures looked at the rate of kidney transplantation and all-cause mortality over the follow-up period of 22 months. People were recruited from three dialysis centers and were asked to complete a Likert scale questionnaire regarding treatment satisfaction, whereas demographic and dialysis treatment data were collated from the electronic records. In contrast to previous research in this area, they found that patient satisfaction was independent of ethnicity and the strongest predictor was dialysis vintage. Mortality was also independent of ethnicity with patient age being the only variable to have a significant effect. Previous Studies Examining Ethnicity and Patient Outcomes The findings of this study are not comparable with previous research examining the role of ethnicity on patient experience and effect on health-related QoL (HRQoL) measures. An earlier Israeli study by Romano-Zelekha et al.3 also looked at QoL measures in Jewish and Arab hemodialysis populations, with questionnaire data collected from cohorts of 558 Jewish and 544 Arab patients across 64 hemodialysis units from January 2014 to February 2015. This larger study demonstrated self-reported lower mental well-being scores in Arab patients, which was accompanied by lower Kidney Disease QoL Short Form scores compared with their Jewish counterparts. They noted no difference in the physical component scores, suggesting similar levels of self-reported overall physical health between the two ethnic groups, supporting the idea that ethnicity and culture bear considerable influence over one's psychosocial experience of chronic disease. A higher reported QoL for both Jewish and Arab hemodialysis populations was found to be associated with higher level of education, higher serum albumin levels, and dialysis access through a fistula or graft. Unlike the study by Cohen-Hagai et al., they did not comment on rates of kidney transplantation or mortality. Ethnic variation of patient-reported outcomes is not just dependent on psychosocial factors but also on national culture and perceptions. A large, multicenter study used data from the Dialysis Outcomes and Practice Patterns Study and the Peritoneal Dialysis Outcomes and Practice Patterns Study cohorts to examine 7771 patients on hemodialysis or peritoneal dialysis (PD) from across six countries around the world.4 They found that Japanese dialysis patients reported better physical health scores compared with patients from the United Kingdom, the United States, Canada, Australia, and New Zealand, whereas those in the United States had higher scores, signifying better mental well-being. When adjusted, these scores did not vary significantly between dialysis modality, although those having PD were noted to have a lower disease burden score. Employment data were also examined in this study, showing that Japanese patients were more likely to maintain employment while receiving dialysis though with a worse mental component score, whereas the opposite was apparent in the United States. Employment and one's ability to maintain this while on KRT is likely to play a significant role in illness perception and feelings of intrusion and therefore could be considered as a surrogate marker for HRQoL. Other ethnic groups have been studied with similar results. Sharma et al.5 conducted a focus group-lead analysis of the experiences of South Asian people on hemodialysis across four units in the United Kingdom, with a high proportion of patients of such heritage. Importantly, the group interviews were conducted in the primary native languages identified from an initial audit of demographics and included Gujarati, Punjabi, and Urdu. Themes comparable with those of dialysis patients overall were identified, such as treatment burden; however, they also noted that South Asian people found it more challenging to foster impactful patient-clinician relationships and had negative ideas about the possibility of transplantation, thus highlighting these as areas for development. In the United States, Black and Hispanic hemodialysis patients were found to have lower physical component scores and HRQoL measures when measured every 6 months over a 5-year period in a study by Kalantar et al.6 They also found that this corresponded with higher mortality rates in these minority ethnic groups, emphasizing the need to implement strategies to improve QoL and subsequently long-term survival. Disparities between ethnic minority groups are not seen solely regarding KRT. Wilkinson et al.7 examined data from 112 studies across 14 countries, focusing on multiple aspects of kidney care including dialysis access, transplantation, outcomes, and end of life care. The review demonstrated disparities in all aspects of the ESKD care continuum, notably quicker progression to ESKD, delayed referral into appropriate renal services, lower rates of transplantation, and higher rates of dissatisfaction with care. These were further compounded by accompanying lower socioeconomic status and lack of availability of suitably matched organs for transplantation. In particular, they noted a scarcity of research in low-resource countries, where ethnic minority groups seen elsewhere may in fact be the majority population, thus further limiting the understanding of the role of ethnicity in kidney care and preventing the opportunity for transferable learning. End of life care was understudied and specifically highlighted as an area where ethnicity and cultural beliefs are highly likely to influence a person's ideas and expectations of care. Ethnicity has implications for pregnancy as well, which should be explored with all women of childbearing age who have a diagnosis of CKD or ESKD. Shah et al.8 noted poorer chance of a successful pregnancy in White ethnicities and in those with ESKD secondary to diabetes, highlighting the need for targeted prepregnancy planning. Limitations of the Study There were a number of limitations to the Israeli study, which the authors do discuss in some detail. Given there are approximately 7000 people receiving hemodialysis in Israel, the sample size was small despite statistical calculation before study enrollment. Even their recruitment of 127 patients did not meet their initial indicated appropriate sample size of 64 people in each ethnic group. It was also noted that there was unequal distribution of ethnicities across the three dialysis centers; therefore, the findings are likely to be less generalizable for health care service planning going forward. There may also have been an element of bias in patient selection, as only those who agreed to participate in the study were included. This bias could potentially be exacerbated further as there was no staff assistance in completing the questionnaires, eliminating those with poor literacy, who were visually impaired, or did not speak Hebrew or Arabic. In evaluating health disparities, these underrepresented groups are often harder to reach and, as such, their needs may not be fully addressed. Given that dialysis vintage was the only predictor of patient satisfaction, the use of a single time-point questionnaire meant that variability and change in this metric over time was not studied. Again, this could be paramount to optimum service planning, including expansion of transplantation programs for appropriate patients. Furthermore, those on PD were not included in this study because of the low numbers of prevalent patients. However, previous evidence has suggested that PD may confer increased patient satisfaction compared with hemodialysis; thus, it could be interesting to examine the interplay between ethnicity, patient satisfaction, and a home-based treatment. Conclusion Previous studies examining the link between ethnicity, culture, and QoL have suggested that ethnic minority groups have different experiences of illness and varying priorities for care and thus have different perceptions of QoL. Ethnicity and illness perception further influence access to health care and, more specifically, access to advanced kidney care planning and KRT. When compared with previous studies, the work by Cohen-Hagai et al. is not in line with previous HRQoL findings in larger cohorts, including a similar study of Israeli hemodialysis patients 10 years ago. Despite this discrepancy, this research highlights the need for further longitudinal qualitative studies across Israel, as well as across other countries worldwide, examining patient-reported outcomes among dialysis patients, including home-based therapies and attitudes toward end of life planning and care.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,025
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,048
Tête enseignante GPT0,433
Écart entre enseignants0,385 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

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Publié2025
Routes d'admission1
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